def fit(self, train_df):
self.ds = pd.Series(train_df.index)
print("Fitting...")
progress_bar = ProgressBar(len(train_df.columns))
for item in train_df.columns:
target = train_df[item].interpolate().bfill()
if self.use_boxcox:
idx = target.index
target, self.lmbda_boxcox[item] = boxcox(target)
target = pd.Series(target, index=idx)
target.index.name = "ds"
target.name = "y"
self.models[item] = pm.auto_arima(
target,
seasonal=False,
exogenous=fourier(
len(target),
seasonality=self.seasonality,
n_terms=self.n_fourier_terms),
method="bfgs",
suppress_warnings=True)
progress_bar.update()
progress_bar.finish()
return self.models
def play_game(trie):
letters = Cubes().shuffle()
board = Board(letters)
print board
print 'You have 3 minutes - press Ctrl-C to finish early'
bar = ProgressBar(60)
try:
event = Event()
for i in range(60):
bar.show(i)
event.wait(3)
bar.finish()
os.system('say "time is up, stop boggling"')
except KeyboardInterrupt:
pass
raw_input('\nPress <ENTER> to see answers')
results = board_search(board, trie)
answers = Answers()
answers.add(results)
print answers
def process_frame_segments(args, segments, width, height):
"""Post-process frame segments to set frame images, etc."""
fn = "process_frame_segments"
globals.log.info("Processing frames...")
frame_segments = [s for s in segments if isinstance(s, FrameSegment)]
n = len(frame_segments)
globals.log.debug("{fn}(): num frames = {n}".format(fn=fn, n=n))
progress = ProgressBar(max_value=n,
quiet=args.quiet or args.debug or n == 0)
progress.update(0)
for i, f in enumerate(frame_segments):
try:
globals.log.debug(
"{fn}(): frame (before) = {b}".format(fn=fn, b=f))
# Frame segments that use a frame from the previous segment.
if (f.input_file == "^"):
if (f.segment_number > 0):
prev = segments[f.segment_number - 1]
globals.log.debug(
"{fn}(): prev = {p}".format(fn=fn, p=prev))
prev.generate_temp_file(args.output, width=width,
height=height)
f.use_frame(
prev.generate_frame(f.frame_number, args.output,
width=width, height=height))
else:
globals.log.error(
"frame segment {s} is attempting to use the last "
"frame of a non-existent previous "
"segment".format(s=f.segment_number))
sys.exit(1)
# Frame segments whose frame comes from a PDF file.
else:
suffix = PurePath(f.input_file).suffix
if (suffix.lower() == ".pdf"):
f.use_frame(f.generate_temp_file(args.output, width=width,
height=height))
else:
globals.log.error(
'unexpected input file type "{s}" for frame segment '
"{f}".format(s=suffix, f=f.segment_number))
sys.exit(1)
progress.update(i)
globals.log.debug("{fn}(): frame (after) = ""{a}".format(fn=fn, a=f))
except SegmentError as e:
progress.finish()
globals.log.exception(e)
sys.exit(1)
else:
progress.finish()
def predict(self, steps=365, freq="D"):
print("Forecasting...")
progress_bar = ProgressBar(len(self.models.items()))
for item, model in self.models.items():
future = model.make_future_dataframe(steps, freq=freq)
pred = model.predict(future).set_index("ds")
pred = pred[["yhat", "yhat_lower", "yhat_upper"]]
self.fcst[item] = pred
if self.use_boxcox:
self.fcst[item] = inv_boxcox(self.fcst[item],
self.lmbda_boxcox[item])
progress_bar.update()
progress_bar.finish()
fcst_df = pd.concat(self.fcst, axis=1).sort_index(axis=1)
return fcst_df
def fit(self, train_df):
print("Fitting...")
progress_bar = ProgressBar(len(train_df.columns))
for item in train_df.columns:
target = train_df[item].dropna()
if self.use_boxcox:
idx = target.index
target, self.lmbda_boxcox[item] = boxcox(target)
target = pd.Series(target, index=idx)
target.index.name = "ds"
target.name = "y"
target = target.reset_index()
self.models[item] = Prophet(**self.prophet_config)
self.models[item].fit(target)
progress_bar.update()
progress_bar.finish()
return self.models
def predict(self, steps=365):
print("Forecasting...")
progress_bar = ProgressBar(len(self.models.items()))
for item, model in self.models.items():
pred = model.predict(
exogenous=fourier(
steps,
seasonality=self.seasonality,
n_terms=self.n_fourier_terms),
n_periods=steps,
return_conf_int=True,
alpha=(1.0 - self.interval_width))
fcst = pd.DataFrame()
fcst["yhat_lower"] = pred[1][:,0]
fcst["yhat"] = pred[0]
fcst["yhat_upper"] = pred[1][:,1]
self.fcst[item] = fcst
if self.use_boxcox:
self.fcst[item] = inv_boxcox(
self.fcst[item],
self.lmbda_boxcox[item])
progress_bar.update()
progress_bar.finish()
return pd.concat(self.fcst, axis=1)
file_size = os.path.getsize(file_path)
expected_part_count = ceil(file_size / PART_SIZE)
print('(' + str(ind) + '/' + str(total) + ') ' + filename + ': ' + str(round(file_size/1024/1024)) + 'MB')
upload_id = glacier.initiate_multipart_upload(file_path)
with open(file_path, mode = 'rb') as archive:
progress_bar = ProgressBar(expected_part_count)
part_ind = 0
content = archive.read(PART_SIZE)
while True:
progress_bar.advance()
try:
if glacier.upload_multipart_part(upload_id, part_ind, content):
archive_id = glacier.complete_multipart_upload(upload_id, archive)
print(filename + ',' + archive_id, file = log)
progress_bar.finish()
break;
else:
content = archive.read(PART_SIZE)
part_ind += 1
except TimeoutError as error:
print(repr(error) + ' skip file')
break;
except KeyboardInterrupt:
print('\nUser stopped!')
sys.exit(0)
archive.close()
ind += 1
log.close()
# find all the files with the allowed extensions
filenames = [f for f in os.listdir(directory) if f.lower().endswith(tuple(exts))]
total = len(filenames)
ind = 1
for filename in filenames:
file_path = directory + '/' + filename
file_size = os.path.getsize(file_path)
expected_part_count = ceil(file_size / PART_SIZE)
print('(' + str(ind) + '/' + str(total) + ') ' + filename + ': ' + str(round(file_size/1024/1024)) + 'MB')
upload_id = glacier.initiate_multipart_upload(file_path)
with open(file_path, mode = 'rb') as archive:
progress_bar = ProgressBar(expected_part_count)
part_ind = 0
content = archive.read(PART_SIZE)
while True:
progress_bar.advance()
if glacier.upload_multipart_part(upload_id, part_ind, content):
archive_id = glacier.complete_multipart_upload(upload_id, archive)
print(filename + ',' + archive_id, file = log)
progress_bar.finish()
break;
else:
content = archive.read(PART_SIZE)
part_ind += 1
archive.close()
ind += 1
log.close()
