def debug_msg(status):
caller_levels = dict(troubleshoot_cleaner=1,
troubleshoot_obj_call=2,
troubleshoot_test=3)
frame = gfi(gof(cf(caller_levels['troubleshoot_test']))[1][0])
print("{} [filename: {}, line: {}]".format(status, frame.filename,
frame.lineno))
def find_date_index(date_to_find, time_list_hours, next_date=0):
c_year, c_month, c_day, c_hour, c_minute = fix_time_list(
date_to_find, single_time_stamp=1)
year, month, day, hour, minute = fix_time_list(time_list_hours)
#print(" supp.%i: searching for %s " % (gf(cf()).lineno, date_to_find))
index = 0
while year[index] < c_year:
index += 1
while month[index] < c_month:
index += 1
while day[index] < c_day and day[index] < 31:
index += 1
while hour[index] < c_hour and hour[index] < 23:
index += 1
while minute[index] < c_minute and minute[index] < 59:
index += 1
if date_to_find != time_list_hours[index]:
if next_date == 0:
#print(" supp.%i: Date %s not found in time list. Returning nothing" % (gf(cf()).lineno, date_to_find))
return "Error"
else:
print(
" supp.%i: Date %s not found in time list. Returning %s instead"
% (gf(cf()).lineno, date_to_find, time_list_hours[index]))
return index
else:
return index
def handle_flagged_args(args, config):
"""handle_flagged_args
This function will execute the appropriate steps for what arguments have been
given. This is, essentially, a good "drop in" point for outside code to use
this module as long as args is a dict within the appropriate format or is an
empty dictionary.
"""
error = None
os_version = str()
os_type = str()
os_category = \
BuildError(args, msg="Invalid build config", frame=gfi(cf()))
# we're building for just one, and it should already be in
# expected_operations...
for item in expected_operations:
if item.name == args['os_type']:
for version in item.versions:
os_version = version
os_type = item.os_type
if item.os_category == 'redhat':
os_category = build_redhat
elif item.os_category == 'debian':
os_category = build_debian
else:
error = False
os_category = os_category.print_msg
break
error = os_category(config, os_version, os_type=os_type) if error \
else error
def build_packages(config):
"""build_packages
Builds for the expected_operations' OperationSettings entries.
This function is intentionaly blind, dumb and stupid to produce what it can.
However, it should also exit with a non-zero when an error does occur to keep
from the caller thinking that things are 'okay' when they are not.
"""
error = 0
print("Building packages...")
for operation in expected_operations:
if operation.os_category == 'redhat':
print("For Redhat...")
function = build_redhat
elif operation.os_category == 'debian':
print("For Debian...")
function = build_debian
else:
# can overwrite a previous error statement, but this does need to
# be preserved...
error = BuildError(operation, msg="Unexpected operation specified",
frame=gfi(cf()))
function = Error.print_msg
# preserve a previous iteration's error, if needed
for os_version in operation.versions:
error = function(config, os_version) if not error else error
if error:
break
if error:
print("A Failure occurred...", str(error))
break
if not error:
print("Completed package builds...")
return error
def timing():
nonlocal previous_time, previous_line
from inspect import currentframe as cf
new_time = time()
new_line = cf().f_back.f_lineno
if previous_time is not None:
print(
f'Took {new_time-previous_time:.3f}s from {previous_line} to {new_line}'
)
previous_time = new_time
previous_line = new_line
def _load_json(fl):
data = {}
if os.path.isfile(fl) and os.access(fl, os.R_OK):
with open(fl, 'r') as fh:
data = json.loads(fh.read())
else:
print("Unable to open JSON file: " + fl)
reason = 'Cannot Access!' if os.path.isfile(fl) else \
"File does not exist!"
print(reason)
raise BuildError(fl, reason, msg="Unable to open JSON file",
frame=gfi(cf()), errnum=errno.ENODATA)
return data
def load_json():
"""load_json
Loads the config.JSON file system object's contents and translate it into a
Python object. This python object is then returned.
"""
try:
env = configure.get_env()
configure.export_to_json(env)
except SystemExit:
raise BuildError(msg="configure failed! Could not build targets!",
frame=gfi(cf()), errno=errno.ENOSYS)
json_fl = os.path.dirname(sys.argv[0]) + "/scripts/config.JSON"
data = _load_json(json_fl)
return data
def _preliminary_construct(config):
# this is an internal function; however, allows for quick and easy access
# to execute these operations...
error = 0
construct_setups._construct_cfgs_from_json(config)
try:
pass
except Exception as error:
print("ASDASD")
return error
except SystemExit:
error = BuildError(
config['setup_cfg'],
config['stdeb_cfg'],
frame=gfi(cf()),
errno=errno.ESPIPE,
msg="Could not construct the setup files"
)
return error
def debug_msg(status):
caller_levels = dict(troubleshoot_cleaner=1, troubleshoot_obj_call=2,
troubleshoot_test=3)
frame = gfi(gof(cf(caller_levels['troubleshoot_test']))[1][0])
print("{} [filename: {}, line: {}]".format(status, frame.filename,
frame.lineno))
# # This works, but using sys path is really not recommended # import os, sys # sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) # # import ../db.py # from Week1.bi2_2_patternCount import PatternCount # # Let's look at the path it gives # from os.path import dirname, abspath # print(dirname(dirname(abspath(__file__)))) # Trying another method, apparently voted as best method if not dealing with packages # note i added in my own aliases from sys import path as sp from inspect import currentframe as cf, getfile as gf from os.path import dirname as odirname, abspath as oabspath currentdir = odirname(oabspath(gf(cf()))) parentdir = odirname(currentdir) sp.insert(0, parentdir) # # This is a shorter form someone made of the above # sys.path.insert(1, os.path.join(sys.path[0], '..')) from Week1.bi2_2_patternCount import PatternCount Text = "ATCAATGATCAACGTAAGCTTCTAAGCATGATCAAGGTGCTCACACAGTTTATCCACAACCTGAGTGGA" "TGACATCAAGATAGGTCGTTGTATCTCCTTCCTCTCGTACTCTCATGACCACGGAAAGATGATCAAGAGAGGATGA" "TTTCTTGGCCATATCGCAATGAATACTTGTGACTTGTGCTTCCAATTGACATCTTCAGCGCCATATTGCGCTGGCC" "AAGGTGACGGAGCGGGATTACGAAAGCATGATCATGGCTGTTGTTCTGTTTATCTTGTTTTGACTGAGACTTGTTA" "GGATAGACGGTTTTTCATCACTGACTAGCCAAAGCCTTACTCTGCCTGACATCGACCGTAAATTGATAATGAATTT" "ACATGCTTCCGCGACGATTTACCTCTTGATCATCGATCCGATTGAAGATCTTCAATTGTTAATTCTCTTGCCTCGA" "CTCATAGCCATGATGAGCTCTTGATCATGTTTCCTTAACCCTCTATTTTTTACGGAAGAATGATCAAGCTGCTGCT"
def clean_series_hour(time_listM,
pricesM,
volumesM,
exc=0,
convert_time_zones=1,
plot_for_extreme=0):
remove_extremes = 1
print(" \033[32;0;0mRunning 'clean_series_hour' ...\033[0;0;0m")
if convert_time_zones: # Flytter nå Coincheck ni timer, men lar Bitstamp stå
if exc == 0 or exc == 5:
n_hours = 1
elif exc == 1:
n_hours = 9
elif exc == 2:
n_hours = 8
elif exc == 3:
n_hours = -5
elif exc == 4:
n_hours = 9
else:
n_hours = 0
print(" Converting time zones: moving series %i hour(s)" % n_hours)
else:
n_hours = 0
if n_hours != 0:
year, month, day, hour, minute = supp.fix_time_list(
time_listM, move_n_hours=n_hours)
time_listM = supp.make_time_list(
year, month, day, hour,
minute) # Lager en ny tidsliste fra de flyttede listene
returnsM = jake_supp.logreturn(pricesM)
time_listH, pricesH, volumesH = convert_to_hour(time_listM, pricesM,
volumesM)
returnsH = jake_supp.logreturn(pricesH)
spread_abs, spreadH, time_list_spread, count_value_error = rolls.rolls(
pricesM, time_listM, calc_basis="h", kill_output=1)
illiq_hours_time, illiqH = ILLIQ.illiq(time_listM,
returnsM,
volumesM,
hourly_or_daily="h",
threshold=0)
rvolH, time_list_rvol = realized_volatility.RVol(time_listM,
pricesM,
daily=0,
annualize=1)
time_list_removed = []
# Removing all hours where Volume is zero
time_listH, time_list_removed, volumesH, spreadH, returnsH, rvolH = supp.remove_list1_zeros_from_all_lists(
time_listH, time_list_removed, volumesH, spreadH, returnsH, rvolH)
print(" dis.%i: Number of hours removed due to zero-volume: %i" %
(gf(cf()).lineno, len(time_list_removed)))
end_time = ""
if exc == 0:
cutoff_date = "01.01.2013 00:00"
start_averaging_date = "01.01.2012 00:00"
elif exc == 1:
cutoff_date = "01.06.2016 00:00"
start_averaging_date = "30.10.2014 00:00"
elif exc == 2:
cutoff_date = "01.01.2013 00:00"
start_averaging_date = "01.01.2012 00:00"
end_time = "29.09.2017 00:00"
elif exc == 3:
cutoff_date = "01.01.2015 00:00"
start_averaging_date = "02.12.2014 00:00"
elif exc == 4:
cutoff_date = "01.01.2014 00:00"
start_averaging_date = "01.10.2013 00:00"
elif exc == 5:
cutoff_date = "01.01.2015 00:00"
start_averaging_date = "01.01.2014 00:00"
else:
print(" TEST SET")
cutoff_date = "01.01.2017 00:00"
start_averaging_date = "01.01.2017 00:00"
cutoff_hour = supp.find_date_index(cutoff_date, time_listH, next_date=1)
start_averaging_hour = supp.find_date_index(start_averaging_date,
time_listH,
next_date=1)
if len(end_time) > 1:
end_hour = supp.find_date_index(end_time, time_listH)
else:
end_hour = len(time_listH) - 1
mean_volume_prev_year = np.average(
volumesH[start_averaging_hour:cutoff_hour])
time_listH = time_listH[cutoff_hour:end_hour]
print(" Time period:", time_listH[0], "to", time_listH[-1])
returnsH = returnsH[cutoff_hour:end_hour]
volumesH = volumesH[cutoff_hour:end_hour]
spreadH = spreadH[cutoff_hour:end_hour]
illiqH = illiqH[cutoff_hour:end_hour]
rvolH = rvolH[cutoff_hour:end_hour]
if plot_for_extreme == 1:
plt.plot(rvolH)
plt.title("Raw rvol")
plt.figure()
plt.plot(spreadH)
plt.title("Raw spreadH")
plt.figure()
plt.plot(volumesH)
plt.title("Raw volume")
plt.figure()
plt.plot(illiqH)
plt.title("Raw illiq")
plt.figure()
plt.plot(returnsH)
plt.title("Raw returnsH")
plt.figure()
hours_to_remove = []
if remove_extremes == 1:
if exc == 0:
hours_to_remove = supp.remove_extremes(hours_to_remove,
returnsH,
0.1,
threshold_lower=-0.1)
hours_to_remove = supp.remove_extremes(hours_to_remove, rvolH, 2)
hours_to_remove = supp.remove_extremes(hours_to_remove, spreadH,
0.01)
hours_to_remove = supp.remove_extremes(hours_to_remove, illiqH,
0.1)
elif exc == 1:
hours_to_remove = supp.remove_extremes(hours_to_remove,
returnsH,
0.075,
threshold_lower=-0.075)
hours_to_remove = supp.remove_extremes(hours_to_remove, rvolH, 2)
hours_to_remove = supp.remove_extremes(hours_to_remove, spreadH,
0.1)
hours_to_remove = supp.remove_extremes(hours_to_remove, illiqH,
0.1)
elif exc == 2:
hours_to_remove = supp.remove_extremes(hours_to_remove,
returnsH,
0.075,
threshold_lower=-0.075)
hours_to_remove = supp.remove_extremes(hours_to_remove, rvolH, 2)
hours_to_remove = supp.remove_extremes(hours_to_remove, spreadH,
0.1)
hours_to_remove = supp.remove_extremes(hours_to_remove, illiqH,
0.1)
elif exc == 3:
hours_to_remove = supp.remove_extremes(hours_to_remove,
returnsH,
0.075,
threshold_lower=-0.075)
hours_to_remove = supp.remove_extremes(hours_to_remove, rvolH, 2)
hours_to_remove = supp.remove_extremes(hours_to_remove, volumesH,
15000)
hours_to_remove = supp.remove_extremes(hours_to_remove, spreadH,
0.1)
hours_to_remove = supp.remove_extremes(hours_to_remove, illiqH,
0.02)
elif exc == 4:
hours_to_remove = supp.remove_extremes(hours_to_remove,
returnsH,
0.075,
threshold_lower=-0.075)
hours_to_remove = supp.remove_extremes(hours_to_remove, rvolH, 2)
hours_to_remove = supp.remove_extremes(hours_to_remove, volumesH,
15000)
hours_to_remove = supp.remove_extremes(hours_to_remove, spreadH,
0.1)
hours_to_remove = supp.remove_extremes(hours_to_remove, illiqH,
0.02)
time_listH = np.delete(time_listH, hours_to_remove)
returnsH = np.delete(returnsH, hours_to_remove)
volumesH = np.delete(volumesH, hours_to_remove)
spreadH = np.delete(spreadH, hours_to_remove)
illiqH = np.delete(illiqH, hours_to_remove)
rvolH = np.delete(rvolH, hours_to_remove)
if plot_for_extreme == 1:
plt.plot(rvolH)
plt.title("rvol")
plt.figure()
plt.plot(spreadH)
plt.title("spreadH")
plt.figure()
plt.plot(volumesH)
plt.title("volume")
plt.figure()
plt.plot(illiqH)
plt.title("illiq")
plt.figure()
plt.plot(returnsH)
plt.title("returnsH")
plt.show()
# Removing all days where Roll is zero
time_listH, time_list_removed, spreadH, volumesH, returnsH, illiqH, rvolH = supp.remove_list1_zeros_from_all_lists(
time_listH, time_list_removed, spreadH, volumesH, returnsH, illiqH,
rvolH)
# Removing all hours where Rvol is zero
time_listH, time_list_removed, rvolH, spreadH, volumesH, returnsH, illiqH = supp.remove_list1_zeros_from_all_lists(
time_listH, time_list_removed, rvolH, spreadH, volumesH, returnsH,
illiqH)
# Removing all hours where ILLIQ is zero
time_listH, time_list_removed, illiqH, rvolH, spreadH, volumesH, returnsH = supp.remove_list1_zeros_from_all_lists(
time_listH, time_list_removed, illiqH, rvolH, spreadH, volumesH,
returnsH)
# Turning ILLIQ, Volume and rvol into log
log_illiqH = np.log(illiqH)
log_volumesH = volume_transformation(volumesH, mean_volume_prev_year)
log_rvolH = np.log(rvolH)
#print(" dis.%i: Length of time %i, spread %i, rvol %i, illiq %i, and log_illiq %i" % (gf(cf()).lineno, len(time_listH), len(spreadH), len(rvolH), len(illiqH), len(log_illiqH)))
print(" \033[32;0;0mFinished running 'clean_series_hour' ...\033[0;0;0m")
return time_listH, returnsH, spreadH, volumesH, log_volumesH, illiqH, log_illiqH, rvolH, log_rvolH
def clean_series_days(time_listM,
pricesM,
volumesM,
exc=0,
print_days_excluded=0,
convert_time_zones=1,
plot_for_extreme=0):
print(" \033[32;0;0mRunning 'clean_series_days' ...\033[0;0;0m")
if convert_time_zones:
if exc == 0 or exc == 5:
n_hours = 1
elif exc == 1:
n_hours = 9
elif exc == 2:
n_hours = 8
elif exc == 3:
n_hours = -5
elif exc == 4:
n_hours = 9
else:
n_hours = 0
print(" Converting time zones: moving series %i hour(s)" % n_hours)
else:
n_hours = 0
if n_hours != 0:
year, month, day, hour, minute = supp.fix_time_list(
time_listM, move_n_hours=n_hours)
time_listM = supp.make_time_list(year, month, day, hour, minute)
time_listD, pricesD, volumesD = convert_to_day(time_listM, pricesM,
volumesM)
end_time_D = ""
if exc == 0:
cutoff_date = "01.01.2013 00:00"
cutoff_min_date = "01.01.2013 01:00"
start_averaging_date = "01.01.2012 00:00"
elif exc == 1:
cutoff_date = "01.01.2016 00:00"
cutoff_min_date = "01.01.2016 09:00"
start_averaging_date = "30.10.2014 00:00"
elif exc == 2:
cutoff_date = "01.01.2013 00:00"
cutoff_min_date = "01.01.2013 08:00"
end_time_D = "01.01.2017 00:00"
end_time_M = "01.01.2017 08:00"
start_averaging_date = "01.01.2012 00:00"
elif exc == 3:
cutoff_date = "01.01.2015 00:00"
cutoff_min_date = "01.01.2015 19:00"
start_averaging_date = "02.12.2014 00:00"
elif exc == 4:
cutoff_date = "01.01.2014 00:00"
cutoff_min_date = "01.01.2014 09:00"
start_averaging_date = "01.10.2013 00:00"
elif exc == 5:
cutoff_date = "01.01.2015 00:00"
cutoff_min_date = "01.01.2015 01:00"
start_averaging_date = "01.03.2014 00:00"
else:
print(" TEST SET")
cutoff_date = "01.01.2017 00:00"
cutoff_min_date = "01.01.2017 00:00"
start_averaging_date = "01.01.2017 00:00"
cutoff_day = supp.find_date_index(cutoff_date, time_listD, next_date=1)
cutoff_min = supp.find_date_index(cutoff_min_date, time_listM, next_date=1)
start_averaging_day = supp.find_date_index(start_averaging_date,
time_listD,
next_date=1)
mean_volume_prev_year = np.average(
volumesD[start_averaging_day:cutoff_day])
if len(end_time_D) > 1:
cutoff_endD = supp.find_date_index(end_time_D, time_listD)
cutoff_endM = supp.find_date_index(end_time_M, time_listM)
else:
cutoff_endD = len(time_listD)
cutoff_endM = len(time_listM)
time_listM = time_listM[cutoff_min:cutoff_endM]
print(" Time period:", time_listM[0], "to",
time_listM[len(time_listM) - 1])
pricesM = pricesM[cutoff_min:cutoff_endM]
volumesM = volumesM[cutoff_min:cutoff_endM]
pricesD = pricesD[cutoff_day:cutoff_endD]
volumesD = volumesD[cutoff_day:cutoff_endD]
time_listD = time_listD[cutoff_day:cutoff_endD]
# Rolls
spread_abs, spreadD, time_list_rolls, count_value_error = rolls.rolls(
pricesM, time_listM, calc_basis="d", kill_output=1)
# Realized volatility
rvolD, RVol_time = realized_volatility.RVol(time_listM,
pricesM,
daily=1,
annualize=1)
# Returns
returnsM = jake_supp.logreturn(pricesM)
returnsD = jake_supp.logreturn(pricesD)
# Amihud's ILLIQ
illiq_timeD, illiqD = ILLIQ.illiq(time_listM,
returnsM,
volumesM,
threshold=0) # Already clean
if plot_for_extreme == 1:
plt.plot(rvolD)
plt.title("Raw rvol")
plt.figure()
plt.plot(spreadD)
plt.title("Raw spreadH")
plt.figure()
plt.plot(volumesD)
plt.title("Raw volume")
plt.figure()
plt.plot(illiqD)
plt.title("Raw illiq")
plt.figure()
plt.plot(returnsD)
plt.title("Raw returnsH")
plt.figure()
time_list_removed = []
# Removing all days where Volume is zero
time_listD, time_list_removed, volumesD, spreadD, returnsD, rvolD = supp.remove_list1_zeros_from_all_lists(
time_listD, time_list_removed, volumesD, spreadD, returnsD, rvolD)
# --------------------------------------------
days_to_remove = []
if exc == 0:
days_to_remove = supp.remove_extremes(days_to_remove,
returnsD,
0.1,
threshold_lower=-0.1)
days_to_remove = supp.remove_extremes(days_to_remove, rvolD, 2)
days_to_remove = supp.remove_extremes(days_to_remove, spreadD, 0.01)
days_to_remove = supp.remove_extremes(days_to_remove, illiqD, 0.1)
elif exc == 1:
days_to_remove = supp.remove_extremes(days_to_remove,
returnsD,
0.1,
threshold_lower=-0.1)
days_to_remove = supp.remove_extremes(days_to_remove, rvolD, 2)
days_to_remove = supp.remove_extremes(days_to_remove, spreadD, 0.01)
days_to_remove = supp.remove_extremes(days_to_remove, illiqD, 0.1)
elif exc == 2:
days_to_remove = supp.remove_extremes(days_to_remove,
returnsD,
0.1,
threshold_lower=-0.1)
days_to_remove = supp.remove_extremes(days_to_remove, rvolD, 2)
days_to_remove = supp.remove_extremes(days_to_remove, spreadD, 0.01)
days_to_remove = supp.remove_extremes(days_to_remove, illiqD, 0.1)
elif exc == 3:
days_to_remove = supp.remove_extremes(days_to_remove,
returnsD,
0.1,
threshold_lower=-0.1)
days_to_remove = supp.remove_extremes(days_to_remove, rvolD, 2)
days_to_remove = supp.remove_extremes(days_to_remove, spreadD, 0.01)
days_to_remove = supp.remove_extremes(days_to_remove, volumesD, 50000)
days_to_remove = supp.remove_extremes(days_to_remove, illiqD, 0.01)
elif exc == 4:
days_to_remove = supp.remove_extremes(days_to_remove,
returnsD,
0.1,
threshold_lower=-0.1)
days_to_remove = supp.remove_extremes(days_to_remove, rvolD, 2)
days_to_remove = supp.remove_extremes(days_to_remove, spreadD, 0.01)
days_to_remove = supp.remove_extremes(days_to_remove, volumesD, 50000)
days_to_remove = supp.remove_extremes(days_to_remove, illiqD, 0.01)
elif exc == 5:
days_to_remove = supp.remove_extremes(days_to_remove,
returnsD,
0.1,
threshold_lower=-0.1)
days_to_remove = supp.remove_extremes(days_to_remove, rvolD, 2)
days_to_remove = supp.remove_extremes(days_to_remove, spreadD, 0.01)
days_to_remove = supp.remove_extremes(days_to_remove, volumesD, 50000)
days_to_remove = supp.remove_extremes(days_to_remove, illiqD, 0.01)
for d in days_to_remove:
time_list_removed = np.append(time_list_removed, time_listD[d])
time_listD = np.delete(time_listD, days_to_remove)
returnsD = np.delete(returnsD, days_to_remove)
volumesD = np.delete(volumesD, days_to_remove)
spreadD = np.delete(spreadD, days_to_remove)
rvolD = np.delete(rvolD, days_to_remove)
illiqD = np.delete(illiqD, days_to_remove)
illiq_timeD = np.delete(illiq_timeD, days_to_remove)
if plot_for_extreme == 1:
plt.plot(rvolD)
plt.title("rvol")
plt.figure()
plt.plot(spreadD)
plt.title("spreadH")
plt.figure()
plt.plot(volumesD)
plt.title("volume")
plt.figure()
plt.plot(illiqD)
plt.title("illiq")
plt.figure()
plt.plot(returnsD)
plt.title("returnsH")
plt.show()
# Removing all days where Roll is zero
time_listD, time_list_removed, spreadD, volumesD, returnsD, \
rvolD, illiqD = supp.remove_list1_zeros_from_all_lists(time_listD, time_list_removed, spreadD, volumesD, returnsD,
rvolD, illiqD)
# Removing all days where Volatility is zero
time_listD, time_list_removed, rvolD, volumesD, returnsD, \
spreadD, illiqD = supp.remove_list1_zeros_from_all_lists(time_listD, time_list_removed, rvolD, volumesD, returnsD,
spreadD, illiqD)
# Turning ILLIQ, Volume and RVol into log
log_illiqD = np.log(illiqD)
log_rvolD = np.log(rvolD)
log_volumesD = volume_transformation(volumesD, mean_volume_prev_year)
print(
" dis.%i: Length of time %i, spread %i, rvol %i, illiq %i, and log_illiq %i"
% (gf(cf()).lineno, len(time_listD), len(spreadD), len(rvolD),
len(illiqD), len(log_illiqD)))
print(" \033[32;0;0m Finished running 'clean_series_days' ...\033[0;0;0m")
return time_listD, returnsD, volumesD, log_volumesD, spreadD, illiqD, log_illiqD, rvolD, log_rvolD
def _set_frame(self, kargs):
if 'frame' in kargs:
self.frame = kargs['frame']
else:
# gof gets a stack of [inner->outer] tuples. tuple[0] is frame
self.frame = gfi(gof(cf())[2][0])
def build_pkg(config, os_type, os_version):
result = _build_package(config, os_type, os_version)
if not result.succeeded:
print(result.cli)
raise RedhatError(result, os_type, os_version, frame=gfi(cf()))
def benchmark_hourly(Y,
time_listH,
HAR_config=0,
hours_in_period=4,
prints=1,
force_max_lag=0):
hours_to_remove = []
if hours_in_period != -1:
X_dummies, n_dummies = time_of_day_dummies(
time_listH,
hours_in_period=hours_in_period) # Dette gir dummy variable
if prints == 1:
print(
" rs.%i: x1 through x%i in the benchmark are time-based dummy variables"
% (gf(cf()).lineno, n_dummies))
else:
n_dummies = 0
n_x = n_dummies
if HAR_config == 1:
AR_order = 1
max_lag = max(AR_order, force_max_lag)
X_AR, hours_to_remove = AR_matrix(Y, time_listH, AR_order,
hours_to_remove)
X_AR = X_AR[max_lag - AR_order:, :]
hours_to_remove = adjust_hours_for_removal(hours_to_remove,
n_hours=(max_lag -
AR_order))
X_HAR = X_AR
n_x += 1
if prints == 1:
print(" rs.%i: x%i through x%i is the X_AR(%i) model" %
(gf(cf()).lineno, n_x, n_x + AR_order - 1, AR_order))
elif HAR_config == 2: # Denne skal inkludere AR(24)
AR_order = 5
max_lag = max(AR_order, force_max_lag)
X_AR, hours_to_remove = AR_matrix(Y, time_listH, AR_order,
hours_to_remove)
print(
" \033[32;0;0mrs.%i: %0.1f percent of hours removed due to AR()\033[0;0;0m"
% (gf(cf()).lineno, 100 * float(len(hours_to_remove) / len(Y))))
X_AR = X_AR[max_lag - AR_order:, :]
hours_to_remove = adjust_hours_for_removal(hours_to_remove,
n_hours=(max_lag -
AR_order))
X_HAR = X_AR
n_x += 1
if prints == 1:
print(" rs.%i: x%i through x%i is the X_AR(%i) model" %
(gf(cf()).lineno, n_x, n_x + AR_order - 1, AR_order))
elif HAR_config == 3: # Denne skal inkludere X_AR(1) verdi 24 timer før, snitt 24 timer før
AR_order = 1
max_lag = max(24, force_max_lag)
X_AR, hours_to_remove = AR_matrix(Y, time_listH, AR_order,
hours_to_remove)
# print(" rs.%i: X_AR is (%i,%i)" % (gf(cf()).lineno, np.size(X_AR,0), np.size(X_AR,1)))
#X_AR = X_AR[max_lag - AR_order:, :] # Hvis max lag er 24, men order=1, så vil vi kutte bort 23 entries til
# print(" rs.%i: X_AR is (%i,%i)" % (gf(cf()).lineno, np.size(X_AR,0), np.size(X_AR,1)))
#hours_to_remove = adjust_hours_for_removal(hours_to_remove, n_hours=(max_lag - AR_order))
lagged_list, index_list_prev_lag, hours_to_remove = get_lagged_list(
Y, time_listH, lag=24, hours_to_remove_prev=hours_to_remove)
# print(" rs.%i: lagged list is (%i)" % (gf(cf()).lineno, len(lagged_list)))
#X_lagged = np.transpose(np.matrix(lagged_list[max_lag:]))
X_lagged = np.transpose(np.matrix(lagged_list))
X_HAR = np.append(X_AR, X_lagged, axis=1)
# print(" rs.%i: X_HAR is (%i,%i)" % (gf(cf()).lineno, np.size(X_HAR,0), np.size(X_HAR,1)))
last_day_average, hours_to_remove = get_last_day_average(
Y,
time_listH,
index_list_prev_lag,
hours_to_remove_prev=hours_to_remove)
# print(" rs.%i: last day average is (%i)" % (gf(cf()).lineno, len(last_day_average)))
#last_day_average = np.transpose(np.matrix(last_day_average[max_lag:]))
last_day_average = np.transpose(np.matrix(last_day_average))
if prints == 1:
n_x += 1
print(" rs.%i: x%i through x%i is the X_AR(%i) model" %
(gf(cf()).lineno, n_x, n_x + AR_order - 1, AR_order))
n_x += AR_order
print(" rs.%i: x%i is the value 24 hours prior" %
(gf(cf()).lineno, n_x))
n_x += 1
print(" rs.%i: x%i is the average for the previous 24 hours" %
(gf(cf()).lineno, n_x))
X_HAR = np.append(X_HAR, last_day_average, axis=1)
elif HAR_config == 4: # Denne skal inkludere verdi 24 timer før, 48 timer før og snitt av 48 timer
1
elif HAR_config == 5: # Denne skal inkludere X_AR(1), verdi 24 timer før, 48 timer før og snitt av 48 timer
AR_order = 1
max_lag = max(48, force_max_lag, AR_order)
X_AR, hours_to_remove = AR_matrix(Y, time_listH, AR_order,
hours_to_remove)
X_AR = X_AR[
max_lag -
AR_order:, :] # Hvis max lag er 24, men order=1, så vil vi kutte bort 23 entries til
hours_to_remove = adjust_hours_for_removal(hours_to_remove,
n_hours=(max_lag -
AR_order))
lagged_list_24, index_list_prev_lag_24 = get_lagged_list(Y,
time_listH,
lag=24)
X_lagged = np.transpose(np.matrix(lagged_list_24[max_lag:]))
lagged_list_48, index_list_prev_lag_48 = get_lagged_list(Y,
time_listH,
lag=48)
X_lagged = np.append(X_lagged,
np.transpose(np.matrix(lagged_list_48[max_lag:])),
axis=1)
X_HAR = np.append(X_AR, X_lagged, axis=1)
last_day_average = get_last_day_average(Y, time_listH,
index_list_prev_lag_48)
last_day_average = np.transpose(np.matrix(last_day_average[max_lag:]))
if prints == 1:
n_x += 1
print(" rs.%i: x%i through x%i is the X_AR(%i) model" %
(gf(cf()).lineno, n_x, n_x + AR_order - 1, AR_order))
n_x += AR_order
print(" rs.%i: x%i is the value 24 hours prior" %
(gf(cf()).lineno, n_x))
n_x += 1
print(" rs.%i: x%i is the value 48 hours prior" %
(gf(cf()).lineno, n_x))
n_x += 1
print(" rs.%i: x%i is the average for the previous 48 hours" %
(gf(cf()).lineno, n_x))
X_HAR = np.append(X_HAR, last_day_average, axis=1)
if hours_in_period != -1:
X_benchmark = X_dummies
else:
X_benchmark = X_HAR
if HAR_config > 0 and hours_in_period != -1:
X_benchmark = np.append(X_benchmark, X_HAR, axis=1)
# if prints == 1:
# print(" rs.%i (END): Y is: %i, X_benchmark is (%i,%i)" % (gf(cf()).lineno, len(Y), np.size(X_benchmark, 0), np.size(X_benchmark, 1)))
return Y, X_benchmark, max_lag, hours_to_remove
