def gen_loaded(tbl):
"""
Variant 1. Generate regular intervals (may be with overlap)
:param tbl:
:return:
"""
cfg['in']['table'] = tbl
# To obtain ``t_intervals_start`` used in query inside gen_data_on_intervals(cfg_out, cfg)
# we copy its content here:
t_prev_interval_start, t_intervals_start = intervals_from_period(
**cfg['in'], period=cfg_out['split_period'])
if cfg['proc']['overlap']:
dt_shifts = np.arange(0, 1, (1 - cfg['proc']['overlap'])) * pd_period_to_timedelta(
cfg_out['split_period'])
t_intervals_start = (
t_intervals_start.to_numpy(dtype="datetime64[ns]")[np.newaxis].T + dt_shifts).flatten()
if cfg['in']['max_date']:
idel = t_intervals_start.searchsorted(
np.datetime64(cfg['in']['max_date'] - pd_period_to_timedelta(cfg_out['split_period'])))
t_intervals_start = t_intervals_start[:idel]
cfg['in']['time_intervals_start'] = t_intervals_start # to save queried time - see main()
cfg_filter = None
cfg_in_columns_saved = cfg['in']['columns']
for start_end in h5q_starts2coord(
cfg['in']['db_path'], cfg['in']['table'], t_intervals_start,
dt_interval=cfg['proc']['dt_interval']
):
a = h5_load_range_by_coord(**cfg['in'], range_coordinates=start_end)
if cfg_filter is None: # only 1 time
# corrects columns if they are not exact mutch to faster h5_load_range_by_coord() next time
cfg['in']['columns'] = a.columns # temporary
# and exclude absent fields to not filter warning of no such column in filt_data_dd()
detect_filt = f"m(ax|in)_({'|'.join(cfg['in']['columns'])})"
cfg_filter = {k: v for k, v in cfg['filter'].items() if re.match(detect_filt, k)}
d, i_burst = filt_data_dd(a, cfg['in']['dt_between_bursts'], cfg['in']['dt_hole_warning'], cfg_filter)
n_bursts = len(i_burst)
if n_bursts > 1: # 1st is always 0
l.info('gaps found: (%s)! at %s', n_bursts - 1, i_burst[1:] - 1)
df0 = d.compute()
if not len(df0):
continue
start_end = df0.index[[0, -1]].values
yield df0, start_end
cfg['in']['columns'] = cfg_in_columns_saved # recover to not affect next file
# path_db = path_cruise / (re.match('(^[\d_]*).*', path_cruise.name).groups()[0].strip('_') + 'incl.h5')
path_db = Path(
r'd:\workData\BalticSea\190713_ABP45\inclinometer\190721incl.h5')
dir_incl = '' if 'inclinometer' in path_db.parent.parts else 'inclinometer/'
probes = [
5, 12
] # [1, 4, 5, 7, 11, 12] #sorted(dt_from_utc_for_probe.keys()) # [1, 4, 5, 7, 11, 12, 14] #[21, 23] #range(1, 40) # [25,26] #, [17, 18]
start = 2
go = True
# Draw in Veusz
if start <= 2 and go: # False: #
dt_custom_s = None # 60 * 5 #None # allow not adjacent intervals with this length, s
t_interval_start, t_intervals_end = intervals_from_period(
datetime_range=np.array(['2019-07-21T20:00:00', '2019-08-18T01:45:00'],
'datetime64[s]'),
period='1D')
# t_interval_start = t_intervals_end[0] - np.timedelta64('5', 'm')
for i, probe in enumerate(probes):
probe_name = f'incl{probe:02}'
print('processing {} intervals...'.format(len(t_intervals_end)))
# for i_interval, (t_interval_start, t_interval_end) in enumerate(zip(pd.DatetimeIndex([t_interval_start]).append(t_intervals_end[:-1]), t_intervals_end), start=1):
for i_interval, (t_interval_end_prev, t_interval_end) in list(
enumerate(zip(
pd.DatetimeIndex([t_interval_start
]).append(t_intervals_end[:-1]),
pd.DatetimeIndex(t_intervals_end)),
start=1))[::-1]:
# if i_interval < 23: #<= 0: # TEMPORARY! Skip this number of intervals
def main(new_arg=None, **kwargs):
"""
:param new_arg: list of strings, command line arguments
:kwargs: dicts of dictcts (for each ini section): specified values overwrites ini values
"""
# global l
cfg = cfg_from_args(my_argparser(), new_arg, **kwargs)
if not cfg['program']:
return # usually error of unrecognized arguments displayed
cfg['in']['db_coefs'] = Path(cfg['in']['db_coefs'])
for path_field in ['db_coefs', 'path_cruise']:
if not cfg['in'][path_field].is_absolute():
cfg['in'][path_field] = (
cfg['in']['cfgFile'].parent / cfg['in'][path_field]
).resolve().absolute() # cfg['in']['cfgFile'].parent /
def constant_factory(val):
def default_val():
return val
return default_val
for lim in ('min_date', 'max_date'):
# convert keys to int because they must be comparable to probes_int_list (for command line arguments keys are allways strings, in yaml you can set string or int)
_ = {int(k): v for k, v in cfg['filter'][lim].items()}
cfg['filter'][lim] = defaultdict(constant_factory(_.get(0)), _)
l = init_logging(logging, None, None, 'INFO')
#l = init_logging(logging, None, cfg['program']['log'], cfg['program']['verbose'])
if True: # False. Experimental speedup but takes memory
from dask.cache import Cache
cache = Cache(2e9) # Leverage two gigabytes of memory
cache.register() # Turn cache on globally
#if __debug__:
# # because there was errors on debug when default scheduler used
# cfg['program']['dask_scheduler'] = 'synchronous'
if cfg['program']['dask_scheduler']:
if cfg['program']['dask_scheduler'] == 'distributed':
from dask.distributed import Client
# cluster = dask.distributed.LocalCluster(n_workers=2, threads_per_worker=1, memory_limit="5.5Gb")
client = Client(processes=False)
# navigate to http://localhost:8787/status to see the diagnostic dashboard if you have Bokeh installed
# processes=False: avoide inter-worker communication for computations releases the GIL (numpy, da.array) # without is error
else:
if cfg['program']['dask_scheduler'] == 'synchronous':
l.warning('using "synchronous" scheduler for debugging')
import dask
dask.config.set(scheduler=cfg['program']['dask_scheduler'])
# Run steps :
st.start = cfg['program']['step_start']
st.end = cfg['program']['step_end']
st.go = True
if not cfg['out'][
'db_name']: # set name by 'path_cruise' name or parent if it has digits at start. priority for name is "*inclinometer*"
for p in (lambda p: [p, p.parent])(cfg['in']['path_cruise']):
m = re.match('(^[\d_]*).*', p.name)
if m:
break
cfg['out']['db_name'] = f"{m.group(1).strip('_')}incl.h5"
dir_incl = next((d for d in cfg['in']['path_cruise'].glob('*inclinometer*')
if d.is_dir()), cfg['in']['path_cruise'])
db_path = dir_incl / '_raw' / cfg['out']['db_name']
# ---------------------------------------------------------------------------------------------
# def fs(probe, name):
# if 'w' in name.lower(): # Baranov's wavegauge electronic
# return 10 # 5
# return 5
# if probe < 20 or probe in [23, 29, 30, 32, 33]: # 30 [4, 11, 5, 12] + [1, 7, 13, 30]
# return 5
# if probe in [21, 25, 26] + list(range(28, 35)):
# return 8.2
# return 4.8
def datetime64_str(time_str: Optional[str] = None) -> np.ndarray:
"""
Reformat time_str to ISO 8601 or to 'NaT'. Used here for input in funcs that converts str to numpy.datetime64
:param time_str: May be 'NaT'
:return: ndarray of strings (tested for 1 element only) formatted by numpy.
"""
return np.datetime_as_string(np.datetime64(time_str, 's'))
probes = cfg['in']['probes'] or range(
1, 41) # sets default range, specify your values before line ---
raw_root, probe_is_incl = re.subn('INCL_?', 'INKL_',
cfg['in']['probes_prefix'].upper())
# some parameters that depends of probe type (indicated by probes_prefix)
p_type = defaultdict(
# baranov's format
constant_factory({
'correct_fun':
partial(correct_txt,
mod_file_name=mod_incl_name,
sub_str_list=[
b'^\r?(?P<use>20\d{2}(\t\d{1,2}){5}(\t\d{5}){8}).*',
b'^.+'
]),
'fs':
10,
'format':
'Baranov',
}),
{
(lambda x: x if x.startswith('incl') else 'incl')(cfg['in']['probes_prefix']):
{
'correct_fun':
partial(
correct_txt,
mod_file_name=mod_incl_name,
sub_str_list=[
b'^(?P<use>20\d{2}(,\d{1,2}){5}(,\-?\d{1,6}){6}(,\d{1,2}\.\d{2})(,\-?\d{1,3}\.\d{2})).*',
b'^.+'
]),
'fs':
5,
'format':
'Kondrashov',
},
'voln': {
'correct_fun':
partial(
correct_txt,
mod_file_name=mod_incl_name,
sub_str_list=[
b'^(?P<use>20\d{2}(,\d{1,2}){5}(,\-?\d{1,8})(,\-?\d{1,2}\.\d{2}){2}).*',
b'^.+'
]),
'fs':
5,
#'tbl_prefix': 'w',
'format':
'Kondrashov',
}
})
if st(1, 'Save inclinometer or wavegage data from ASCII to HDF5'):
# Note: Can not find additional not corrected files for same probe if already have any corrected in search path (move them out if need)
i_proc_probe = 0 # counter of processed probes
i_proc_file = 0 # counter of processed files
# patten to identify only _probe_'s raw data files that need to correct '*INKL*{:0>2}*.[tT][xX][tT]':
raw_parent = dir_incl / '_raw' # raw_parent /=
if cfg['in']['raw_subdir'] is None:
cfg['in']['raw_subdir'] = ''
dir_out = raw_parent / re.sub(r'[.\\/ *?]', '_',
cfg['in']['raw_subdir'])
# sub replaces multilevel subdirs to 1 level that correct_fun() can only make
def dt_from_utc_2000(probe):
""" Correct time of probes started without time setting. Raw date must start from 2000-01-01T00:00"""
return (
datetime(year=2000, month=1, day=1) -
cfg['in']['time_start_utc'][probe]
) if cfg['in']['time_start_utc'].get(probe) else timedelta(0)
# convert cfg['in']['dt_from_utc'] keys to int
cfg['in']['dt_from_utc'] = {
int(p): v
for p, v in cfg['in']['dt_from_utc'].items()
}
# convert cfg['in']['t_start_utc'] to cfg['in']['dt_from_utc'] and keys to int
cfg['in']['dt_from_utc'].update( # overwriting the 'time_start_utc' where already exist
{int(p): dt_from_utc_2000(p) for p, v in cfg['in']['time_start_utc'].items()}
)
# make cfg['in']['dt_from_utc'][0] be default value
cfg['in']['dt_from_utc'] = defaultdict(
constant_factory(cfg['in']['dt_from_utc'].pop(0, timedelta(0))),
cfg['in']['dt_from_utc'])
for probe in probes:
raw_found = []
raw_pattern_file = str(
Path(glob.escape(cfg['in']['raw_subdir'])) /
cfg['in']['raw_pattern'].format(prefix=raw_root, number=probe))
correct_fun = p_type[cfg['in']['probes_prefix']]['correct_fun']
# if not archive:
if (not re.match(r'.*(\.zip|\.rar)$', cfg['in']['raw_subdir'],
re.IGNORECASE)) and raw_parent.is_dir():
raw_found = list(raw_parent.glob(raw_pattern_file))
if not raw_found:
# Check if already have corrected files for probe generated by correct_txt(). If so then just use them
raw_found = list(
dir_out.glob(
f"{cfg['in']['probes_prefix']}{probe:0>2}.txt"))
if raw_found:
print('corrected csv file', [r.name for r in raw_found],
'found')
correct_fun = lambda x, dir_out: x
elif not cfg['in']['raw_subdir']:
continue
for file_in in (raw_found or open_csv_or_archive_of_them(
raw_parent, binary_mode=False, pattern=raw_pattern_file)):
file_in = correct_fun(file_in, dir_out=dir_out)
if not file_in:
continue
tbl = file_in.stem # f"{cfg['in']['probes_prefix']}{probe:0>2}"
# tbl = re.sub('^((?P<i>inkl)|w)_0', lambda m: 'incl' if m.group('i') else 'w', # correct name
# re.sub('^[\d_]*|\*', '', file_in.stem).lower()), # remove date-prefix if in name
csv2h5(
[
str(
Path(__file__).parent / 'ini' /
f"csv_{'inclin' if probe_is_incl else 'wavegage'}_{p_type[cfg['in']['probes_prefix']]['format']}.ini"
),
'--path',
str(file_in),
'--blocksize_int',
'50_000_000', # 50Mbt
'--table',
tbl,
'--db_path',
str(db_path),
# '--log', str(scripts_path / 'log/csv2h5_inclin_Kondrashov.log'),
# '--b_raise_on_err', '0', # ?
'--b_interact',
'0',
'--fs_float',
str(p_type[cfg['in']['probes_prefix']]
['fs']), #f'{fs(probe, file_in.stem)}',
'--dt_from_utc_seconds',
str(cfg['in']['dt_from_utc'][probe].total_seconds()),
'--b_del_temp_db',
'1',
] +
(['--csv_specific_param_dict', 'invert_magnitometr: True']
if probe_is_incl else []),
**{
'filter': {
'min_date':
cfg['filter']['min_date'].get(
probe, np.datetime64(0, 'ns')),
'max_date':
cfg['filter']['max_date'].get(
probe, np.datetime64('now', 'ns')
), # simple 'now' works in sinchronious mode
}
})
# Get coefs:
l.info(
f"Adding coefficients to {db_path}/{tbl} from {cfg['in']['db_coefs']}"
)
try:
h5copy_coef(cfg['in']['db_coefs'], db_path, tbl)
except KeyError as e: # Unable to open object (component not found)
l.warning(
'No coefs to copy?'
) # write some dummy coefficients to can load Veusz patterns:
h5copy_coef(None,
db_path,
tbl,
dict_matrices=dict_matrices_for_h5(tbl=tbl))
except OSError as e:
l.warning(
'Not found DB with coefs?'
) # write some dummy coefficients to can load Veusz patterns:
h5copy_coef(None,
db_path,
tbl,
dict_matrices=dict_matrices_for_h5(tbl=tbl))
i_proc_file += 1
else:
print('no', raw_pattern_file, end=', ')
i_proc_probe += 1
print('Ok:', i_proc_probe, 'probes,', i_proc_file, 'files processed.')
if st(2, 'Calculate physical parameters and average'):
kwarg = {
'in': {
'min_date': cfg['filter']['min_date'][0],
'max_date': cfg['filter']['max_date'][0],
'time_range_zeroing': cfg['in']['time_range_zeroing']
},
'proc': {}
}
# if aggregate_period_s is None then not average and write to *_proc_noAvg.h5 else loading from that h5 and writing to _proc.h5
if not cfg['out']['aggregate_period_s']:
cfg['out']['aggregate_period_s'] = [
None, 2, 600, 7200 if probe_is_incl else 3600
]
if cfg['in']['azimuth_add']:
if 'Lat' in cfg['in']['azimuth_add']:
# add magnetic declination,° for used coordinates
# todo: get time
kwarg['proc']['azimuth_add'] = mag_dec(
cfg['in']['azimuth_add']['Lat'],
cfg['in']['azimuth_add']['Lon'],
datetime(2020, 9, 10),
depth=-1)
else:
kwarg['proc']['azimuth_add'] = 0
if 'constant' in cfg['in']['azimuth_add']:
# and add constant. For example, subtruct declination at the calibration place if it was applied
kwarg['proc']['azimuth_add'] += cfg['in']['azimuth_add'][
'constant'] # add -6.656 to account for calibration in Kaliningrad (mag deg = 6.656°)
for aggregate_period_s in cfg['out']['aggregate_period_s']:
if aggregate_period_s is None:
db_path_in = db_path
db_path_out = dir_incl / f'{db_path.stem}_proc_noAvg.h5'
else:
db_path_in = dir_incl / f'{db_path.stem}_proc_noAvg.h5'
db_path_out = dir_incl / f'{db_path.stem}_proc.h5' # or separately: '_proc{aggregate_period_s}.h5'
# 'incl.*|w\d*' inclinometers or wavegauges w\d\d # 'incl09':
tables_list_regex = f"{cfg['in']['probes_prefix'].replace('voln', 'w')}.*"
if cfg['in']['probes']:
tables_list_regex += "(?:{})".format('|'.join(
'{:0>2}'.format(p) for p in cfg['in']['probes']))
args = [
'../../empty.yml', # all settings are here, so to not print 'using default configuration' we use some existed empty file
'--db_path',
str(db_path_in),
'--tables_list',
tables_list_regex,
'--aggregate_period',
f'{aggregate_period_s}S' if aggregate_period_s else '',
'--out.db_path',
str(db_path_out),
'--table',
f'V_incl_bin{aggregate_period_s}'
if aggregate_period_s else 'V_incl',
'--verbose',
'INFO', #'DEBUG' get many numba messages
'--b_del_temp_db',
'1',
# '--calc_version', 'polynom(force)', # depreshiated
# '--chunksize', '20000',
# '--not_joined_h5_path', f'{db_path.stem}_proc.h5',
]
if aggregate_period_s is None: # proc. parameters (if we have saved proc. data then when aggregating we are not processing)
# Note: for Baranov's prog 4096 is not suited:
args += ([
'--max_dict',
'M[xyz]:4096',
# '--time_range_zeroing_dict', "incl19: '2019-11-10T13:00:00', '2019-11-10T14:00:00'\n," # not works - use kwarg
# '--time_range_zeroing_list', '2019-08-26T04:00:00, 2019-08-26T05:00:00'
'--split_period',
'1D'
] if probe_is_incl else [
'--bad_p_at_bursts_starts_peroiod',
'1H',
])
# csv splitted by 1day (default for no avg) else csv is monolith
if aggregate_period_s not in cfg['out'][
'aggregate_period_s_not_to_text']: # , 300, 600]:
args += ['--text_path', str(dir_incl / 'text_output')]
# If need all data to be combined one after one:
# set_field_if_no(kwarg, 'in', {})
# kwarg['in'].update({
#
# 'tables': [f'incl{i:0>2}' for i in min_date.keys() if i!=0],
# 'dates_min': min_date.values(), # in table list order
# 'dates_max': max_date.values(), #
# })
# set_field_if_no(kwarg, 'out', {})
# kwarg['out'].update({'b_all_to_one_col': 'True'})
incl_h5clc.main(args, **kwarg)
if st(3, 'Calculate spectrograms'): # Can be done at any time after step 1
min_Pressure = 7
# add dict dates_min like {probe: parameter} of incl_clc to can specify param to each probe
def raise_ni():
raise NotImplementedError(
'Can not proc probes having different fs in one run: you need to do it separately'
)
args = [
Path(incl_h5clc.__file__).with_name(
f'incl_h5spectrum{db_path.stem}.yaml'),
# if no such file all settings are here
'--db_path',
str(dir_incl / f'{db_path.stem}_proc_noAvg.h5'),
'--tables_list',
f"{cfg['in']['probes_prefix']}.*", # inclinometers or wavegauges w\d\d ## 'w02', 'incl.*',
# '--aggregate_period', f'{aggregate_period_s}S' if aggregate_period_s else '',
'--min_date',
datetime64_str(cfg['filter']['min_date'][0]),
'--max_date',
datetime64_str(cfg['filter']['max_date']
[0]), # '2019-09-09T16:31:00', #17:00:00
'--min_Pressure',
f'{min_Pressure}',
# '--max_dict', 'M[xyz]:4096', # use if db_path is not ends with _proc_noAvg.h5 i.e. need calc velocity
'--out.db_path',
f"{db_path.stem.replace('incl', cfg['in']['probes_prefix'])}_proc_psd.h5",
# '--table', f'psd{aggregate_period_s}' if aggregate_period_s else 'psd',
'--fs_float',
str(p_type[cfg['in']['probes_prefix']]
['fs']), # f"{fs(probes[0], cfg['in']['probes_prefix'])}",
# (lambda x: x == x[0])(np.vectorize(fs)(probes, prefix))).all() else raise_ni()
#
# '--time_range_zeroing_list', '2019-08-26T04:00:00, 2019-08-26T05:00:00'
# '--verbose', 'DEBUG',
# '--chunksize', '20000',
'--b_interact',
'0',
]
if probe_is_incl:
args += [
'--split_period',
'2H',
'--fmin',
'0.0004', #0.0004
'--fmax',
'1.05'
]
else:
args += [
'--split_period',
'1H',
'--dt_interval_minutes',
'15', # set this if burst mode to the burst interval
'--fmin',
'0.0001',
'--fmax',
'4',
#'--min_Pressure', '-1e15', # to not load NaNs
]
incl_h5spectrum.main(args)
if st(4, 'Draw in Veusz'):
pattern_path = dir_incl / r'processed_h5,vsz/201202-210326incl_proc#28.vsz'
# r'\201202_1445incl_proc#03_pattern.vsz' #'
# db_path.parent / r'vsz_5min\191119_0000_5m_incl19.vsz' # r'vsz_5min\191126_0000_5m_w02.vsz'
b_images_only = False
# importing in vsz index slices replacing:
pattern_str_slice_old = None
# Length of not adjacent intervals, s (set None to not allow)
# pandas interval in string or tuple representation '1D' of period between intervals and interval to draw
period_str = '0s' # '1D' # dt
dt_str = '0s' # '5m'
file_intervals = None
period = to_offset(period_str).delta
dt = to_offset(dt_str).delta # timedelta(0) # 60 * 5
if file_intervals and period and dt:
# Load starts and assign ends
t_intervals_start = pd.read_csv(
cfg['in']['path_cruise'] /
r'vsz+h5_proc\intervals_selected.txt',
converters={
'time_start': lambda x: np.datetime64(x, 'ns')
},
index_col=0).index
edges = (pd.DatetimeIndex(t_intervals_start),
pd.DatetimeIndex(t_intervals_start + dt_custom_s)
) # np.zeros_like()
elif period and dt:
# Generate periodic intervals
t_interval_start, t_intervals_end = intervals_from_period(
datetime_range=np.array(
[
cfg['filter']['min_date']['0'],
cfg['filter']['max_date']['0']
],
# ['2018-08-11T18:00:00', '2018-09-06T00:00:00'],
# ['2019-02-11T13:05:00', '2019-03-07T11:30:00'],
# ['2018-11-16T15:19', '2018-12-14T14:35'],
# ['2018-10-22T12:30', '2018-10-27T06:30:00'],
'datetime64[s]'),
period=period)
edges = (pd.DatetimeIndex([t_interval_start
]).append(t_intervals_end[:-1]),
pd.DatetimeIndex(t_intervals_end))
else: # [min, max] edges for each probe
edges_dict = {
pr:
[cfg['filter']['min_date'][pr], cfg['filter']['max_date'][pr]]
for pr in probes
}
cfg_vp = {'veusze': None}
for i, probe in enumerate(probes):
# cfg_vp = {'veusze': None}
if edges_dict: # custom edges for each probe
edges = [pd.DatetimeIndex([t]) for t in edges_dict[probe]]
# substr in file to rerplace probe_name_in_pattern (see below).
probe_name = f"_{cfg['in']['probes_prefix'].replace('incl', 'i')}{probe:02}"
tbl = None # f"/{cfg['in']['probes_prefix']}{probe:02}" # to check probe data exist in db else will not check
l.info('Draw %s in Veusz: %d intervals...', probe_name,
edges[0].size)
# for i_interval, (t_interval_start, t_interval_end) in enumerate(zip(pd.DatetimeIndex([t_interval_start]).append(t_intervals_end[:-1]), t_intervals_end), start=1):
for i_interval, (t_interval_start,
t_interval_end) in enumerate(zip(*edges),
start=1):
# if i_interval < 23: #<= 0: # TEMPORARY Skip this number of intervals
# continue
if period and period != dt:
t_interval_start = t_interval_end - pd.Timedelta(
dt_custom_s, 's')
if tbl:
try: # skipping absent probes
start_end = h5q_interval2coord(
db_path=str(db_path),
table=tbl,
t_interval=(t_interval_start, t_interval_end))
if not len(start_end):
break # no data
except KeyError:
break # device name not in specified range, go to next name
pattern_path_new = pattern_path.with_name(''.join([
f'{t_interval_start:%y%m%d_%H%M}',
f'_{dt_str}' if dt else '', f'{probe_name}.vsz'
]))
# Modify pattern file
if not b_images_only:
pattern_type, pattern_number = re.match(
r'.*(incl|w)_proc?#?(\d*).*',
pattern_path.name).groups()
probe_name_in_pattern = f"_{pattern_type.replace('incl', 'i')}{pattern_number}"
def f_replace(line):
"""
Replace in file
1. probe name
2. slice
"""
# if i_interval == 1:
line, ok = re.subn(probe_name_in_pattern, probe_name,
line)
if ok and pattern_str_slice_old: # can be only in same line
str_slice = '(({:d}, {:d}, None),)'.format(
*(start_end +
np.int32([-1, 1]))) # bytes(, 'ascii')
line = re.sub(pattern_str_slice_old, str_slice,
line)
return line
if not rep_in_file(pattern_path,
pattern_path_new,
f_replace=f_replace,
binary_mode=False):
l.warning('Veusz pattern not changed!'
) # may be ok if we need draw pattern
# break
elif cfg_vp['veusze']:
cfg_vp['veusze'].Load(str(pattern_path_new))
elif cfg_vp['veusze']:
cfg_vp['veusze'].Load(str(pattern_path_new))
txt_time_range = \
"""
"[['{:%Y-%m-%dT%H:%M}', '{:%Y-%m-%dT%H:%M}']]" \
""".format(t_interval_start, t_interval_end)
print(f'{i_interval}. {txt_time_range}', end=' ')
cfg_vp = veuszPropagate.main(
[
Path(veuszPropagate.__file__).parent.with_name(
'veuszPropagate.ini'),
# '--data_yield_prefix', '-',
# '--path', str(db_path), # if custom loading from db and some source is required
'--tables_list',
'', # switches to search vsz-files only # f'/{probe_name}', # 181022inclinometers/ \d*
'--pattern_path',
str(pattern_path_new),
# fr'd:\workData\BalticSea\190801inclinometer_Schuka\{probe_name}_190807_1D.vsz',
# str(dir_incl / f'{probe_name}_190211.vsz'), #warning: create file with small name
# '--before_next', 'restore_config',
# '--add_to_filename', f"_{t_interval_start:%y%m%d_%H%M}_{dt}",
'--filename_fun',
f'lambda tbl: "{pattern_path_new.name}"',
'--add_custom_list',
f'USEtime__', # f'USEtime{probe_name}', nAveragePrefer',
'--add_custom_expressions_list',
txt_time_range,
# + """
# ", 5"
# """,
'--b_update_existed',
'True',
'--export_pages_int_list',
'0', # 0 for all '6, 7, 8', #'1, 2, 3'
# '--export_dpi_int', '200',
'--export_format',
'jpg', #'emf',
'--b_interact',
'0',
'--b_images_only',
f'{b_images_only}',
'--return',
'<embedded_object>', # reuse to not bloat memory
'--b_execute_vsz',
'True',
'--before_next',
'Close()' # Close() need if b_execute_vsz many files
],
veusze=cfg_vp['veusze'])
if st(40, f'Draw in Veusz by loader-drawer.vsz method'):
# save all vsz files that uses separate code
from os import chdir as os_chdir
dt_s = 300
cfg['in'][
'pattern_path'] = db_path.parent / f'vsz_{dt_s:d}s' / '~pattern~.vsz'
time_starts = pd.read_csv(
db_path.parent / r'processed_h5,vsz' / 'intervals_selected.txt',
index_col=0,
parse_dates=True,
date_parser=lambda x: pd.to_datetime(x, format='%Y-%m-%dT%H:%M:%S'
)).index
pattern_code = cfg['in']['pattern_path'].read_bytes(
) # encoding='utf-8'
path_vsz_all = []
for i, probe in enumerate(probes):
probe_name = f"{cfg['in']['probes_prefix']}{probe:02}" # table name in db
l.info('Draw %s in Veusz: %d intervals...', probe_name,
time_starts.size)
for i_interval, time_start in enumerate(time_starts, start=1):
path_vsz = cfg['in']['pattern_path'].with_name(
f"{time_start:%y%m%d_%H%M}_{probe_name.replace('incl','i')}.vsz"
)
# copy file to path_vsz
path_vsz.write_bytes(pattern_code) # replaces 1st row
path_vsz_all.append(path_vsz)
os_chdir(cfg['in']['pattern_path'].parent)
veuszPropagate.main(
[
'ini/veuszPropagate.ini',
'--path',
str(cfg['in']['pattern_path'].with_name(
'??????_????_*.vsz')), # db_path),
'--pattern_path',
f"{cfg['in']['pattern_path']}_",
# here used to auto get export dir only. may not be _not existed file path_ if ['out']['paths'] is provided
# '--table_log', f'/{device}/logRuns',
# '--add_custom_list', f'{device_veusz_prefix}USE_time_search_runs', # 'i3_USE_timeRange',
# '--add_custom_expressions',
# """'[["{log_row[Index]:%Y-%m-%dT%H:%M:%S}", "{log_row[DateEnd]:%Y-%m-%dT%H:%M:%S}"]]'""",
# '--export_pages_int_list', '1', #'--b_images_only', 'True'
'--b_interact',
'0',
'--b_update_existed',
'True', # todo: delete_overlapped
'--b_images_only',
'True',
'--load_timeout_s_float',
str(cfg['program']['load_timeout_s'])
# '--min_time', '2020-07-08T03:35:00',
],
**{'out': {
'paths': path_vsz_all
}})
if st(50, 'Export from existed Veusz files in dir'):
pattern_parent = db_path.parent # r'vsz_5min\191126_0000_5m_w02.vsz''
pattern_path = str(pattern_parent / r'processed_h5,vsz' /
'??????incl_proc#[1-9][0-9].vsz') # [0-2,6-9]
veuszPropagate.main([
'ini/veuszPropagate.ini',
'--path',
pattern_path,
'--pattern_path',
pattern_path,
# '--export_pages_int_list', '1', #'--b_images_only', 'True'
'--b_interact',
'0',
'--b_update_existed',
'True', # todo: delete_overlapped
'--b_images_only',
'True',
'--load_timeout_s_float',
str(cfg['program']['load_timeout_s']),
'--b_execute_vsz',
'True',
'--before_next',
'Close()' # Close() need if b_execute_vsz many files
])
except KeyError as e: # Unable to open object (component not found)
pass
else:
print('Coef is not copied!')
# todo write some dummy coefficients to can load Veusz patterns
i_proc += 1
print(f'Ok! ({i_proc} probes processed)')
# Draw in Veusz
if start == 2:
# Not adjacent intervals with this length, s (set None to not allow)
dt_custom_s = None # 60 * 5 # None
# Periodic intervals
t_interval_start, t_intervals_end = intervals_from_period(
datetime_range=np.array(
['2019-08-11T18:00:00', '2019-09-06T00:00:00'],
# ['2019-07-28T00:00:00', '2019-09-11T00:00:00'], #08-14 07-28T00:00:00
'datetime64[s]'),
period='1D')
# t_interval_start = t_intervals_end[0] - np.timedelta64('5', 'm')
for i, probe in enumerate(probes):
probe_name = f'incl{probe:02}'
print('processing {} intervals...'.format(len(t_intervals_end)))
# for i_interval, (t_interval_start, t_interval_end) in enumerate(zip(pd.DatetimeIndex([t_interval_start]).append(t_intervals_end[:-1]), t_intervals_end), start=1):
cfg_vp = {'veusze': None}
for i_interval, (t_interval_end_prev,
t_interval_end) in enumerate(zip(
pd.DatetimeIndex([t_interval_start
]).append(t_intervals_end[:-1]),
pd.DatetimeIndex(t_intervals_end)),
start=1): # list()[::-1]
from utils_time import intervals_from_period
path_cruise = drive_d.joinpath('workData/BalticSea/190210/')
dir_probe = 'ADV_Nortek-Vector/'
start = 1
go = True
# ---------------------------------------------------------------------------------------------
if start <= 1 and go: # False: #
dt_custom_s = None # 60 * 5 # allow not adjacent intervals with this length, s
t_interval_start, t_intervals_end = intervals_from_period(
datetime_range=np.array(
['2019-02-13T00:00:00', '2019-02-25T00:00:00'],
# ['2018-11-16T15:19', '2018-12-14T14:35'],
# ['2018-10-22T12:30', '2018-10-27T06:30:00'],
'datetime64[s]'),
period='1D')
# t_intervals_end = pd.DatetimeIndex(['2018-11-19T00:00', '2018-11-30T00:00', '2018-12-05T00:00'])
# t_interval_start = t_intervals_end[0]
# t_intervals_end = t_intervals_end[1:]
# '2018-12-09T03:10', '2018-12-06T21:30', '2018-12-11T11:40',
# t_intervals_end = pd.DatetimeIndex(['2018-10-22T12:30', '2018-10-27T06:30:00'])
probe_name = 'Vector'
file_pattern = f'1D_{probe_name}_190212.vsz' * 70
print('processing {} intervals...'.format(len(t_intervals_end)))
for i_interval, (t_interval_start, t_interval_end) in enumerate(zip(
pd.DatetimeIndex([t_interval_start]).append(t_intervals_end[:-1]),